no code implementations • 3 May 2024 • Siddhant Kharbanda, Devaansh Gupta, Erik Schultheis, Atmadeep Banerjee, Cho-Jui Hsieh, Rohit Babbar
Extreme Multi-label Text Classification (XMC) involves learning a classifier that can assign an input with a subset of most relevant labels from millions of label choices.
Extreme Multi-Label Classification Multi Label Text Classification +3
2 code implementations • 29 Jan 2024 • Erik Schultheis, Wojciech Kotłowski, Marek Wydmuch, Rohit Babbar, Strom Borman, Krzysztof Dembczyński
We consider the optimization of complex performance metrics in multi-label classification under the population utility framework.
2 code implementations • NeurIPS 2023 • Erik Schultheis, Marek Wydmuch, Wojciech Kotłowski, Rohit Babbar, Krzysztof Dembczyński
As such, it is characterized by long-tail labels, i. e., most labels have very few positive instances.
no code implementations • 6 Jun 2023 • Erik Schultheis, Rohit Babbar
In classification problems with large output spaces (up to millions of labels), the last layer can require an enormous amount of memory.
1 code implementation • 28 Mar 2023 • David Melching, Erik Schultheis, Eric Breitbarth
Digital image correlation (DIC) has become a valuable tool to monitor and evaluate mechanical experiments of cracked specimen, but the automatic detection of cracks is often difficult due to inherent noise and artefacts.
no code implementations • 29 Oct 2022 • Siddhant Kharbanda, Atmadeep Banerjee, Erik Schultheis, Rohit Babbar
We thus propose CascadeXML, an end-to-end multi-resolution learning pipeline, which can harness the multi-layered architecture of a transformer model for attending to different label resolutions with separate feature representations.
Extreme Multi-Label Classification Multi Label Text Classification +2
no code implementations • 26 Jul 2022 • Erik Schultheis, Marek Wydmuch, Rohit Babbar, Krzysztof Dembczyński
The propensity model introduced by Jain et al. 2016 has become a standard approach for dealing with missing and long-tail labels in extreme multi-label classification (XMLC).
no code implementations • 27 Sep 2021 • Erik Schultheis, Rohit Babbar
We want to start in a region of weight space a) with low loss value, b) that is favourable for second-order optimization, and c) where the conjugate-gradient (CG) calculations can be performed quickly.
no code implementations • 23 Sep 2021 • Erik Schultheis, Rohit Babbar
This paper considers binary and multilabel classification problems in a setting where labels are missing independently and with a known rate.
no code implementations • 1 Jul 2020 • Erik Schultheis, Mohammadreza Qaraei, Priyanshu Gupta, Rohit Babbar
In addition to the computational burden arising from large number of training instances, features and labels, problems in XMC are faced with two statistical challenges, (i) large number of 'tail-labels' -- those which occur very infrequently, and (ii) missing labels as it is virtually impossible to manually assign every relevant label to an instance.